Revisiting the Internet of Things: New Trends, Opportunities and Grand Challenges

The Internet of Things (IoT) has brought the dream of ubiquitous data access from physical environments into reality. IoT embeds sensors and actuators in physical objects so that they can communicate and exchange data between themselves to improve efficiency along with enabling real-time intelligent services and offering better quality of life to people. The number of deployed IoT devices has rapidly grown in the past five years in a way that makes IoT the most disruptive technology in recent history. In this paper, we reevaluate the position of IoT in our life and provide deep insights on its enabling technologies, applications, rising trends and grand challenges. The paper also highlights the role of artificial intelligence to make IoT the top transformative technology that has been ever developed in human history

Location Transparency Call (LTC) System: An Intelligent Phone Dialing System Based on the Phone of Things (PoT) Architecture

Phone of Things (PoT) extends the connectivity options for IoT systems by leveraging the ubiquitous phone network infrastructure, making it part of the IoT architecture. PoT enriches the connectivity options of IoT while promoting its affordability, accessibility, security, and scalability. PoT enables incentive IoT applications that can result in more innovative homes, office environments, and telephony solutions. This paper presents the Location Transparency Call (LTC) system, an intelligent phone dialing system for businesses based on the PoT architecture. The LTC system intelligently mitigates the impact of missed calls on companies and provides high availability and dynamic reachability to employees within the premises. LTC automatically forwards calls to the intended employees to the closest phone extensions at their current locations. Location transparency is achieved by actively maintaining and dynamically updating a real-time database that maps the persons’ locations using the RFID tags they carry. We demonstrate the system’s feasibility and usability and evaluate its performance through a fully-fledged prototype representing its hardware and software components that can be applied in real situations at large scale

A hybrid approach to automatic IaaS service selection

Abstract Cloud computing provides on-demand resources and removes the boundaries of resources’ physical locations. By providing virtualized computing resources in an elastic manner over the internet, IaaS providers allow organizations to save upfront infrastructure costs and focus on features that discriminate their businesses. The growing number of providers makes manual selection of the most suitable configuration of IaaS resources, or IaaS services, difficult and time consuming while requiring a high level of expertise. In our previous paper we proposed QuARAM recommender, a general platform for automatic IaaS service selection. In this paper, we present in detail the hybrid approach to automatic service selection used in our platform. The selection process begins with automatic extraction of an application’s features, requirements and preferences, which are then used to produce a list of potential services for the application’s deployment. We use case-based reasoning and MCDM (Multi-criteria Decision Making) to provide a recommendation of suitable services for application deployment, clustering to handle the problem of a large search space and a service consolidation method to improve the resource utilization and decrease the total service price. We carry out a case study with a prototype implementation of our platform to demonstrate that automatic IaaS service selection using a combination of all the proposed approaches is both practical and achievable

Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities

In an effort to reach accident-free milestones or drastically reduce/eliminate road fatalities rates and traffic congestion and to create disruptive, transformational mobility systems and services, different parties (e.g., automakers, universities, governments, and road traffic regulators) have collaborated to research, develop, and test connected vehicle (CV) technologies. CVs create new data-rich environments and are considered key enablers for many applications and services that will make our roads safer, less congested, and more eco-friendly. A deeper understanding of the CV technologies will pave the way to avoid setbacks and will help in developing more innovative applications and breakthroughs. In the CV paradigm, vehicles become smarter by communicating with nearby vehicles, connected infrastructure, and the surroundings. This connectivity will be substantial to support different features and systems, such as adaptive routing, real-time navigation, and slow and near real-time infrastructure. Further examples include environmental sensing, advanced driver-assistance systems, automated driving systems, mobility on demand, and mobility as a service. This article provides a comprehensive review on CV technologies including fundamental challenges, state-of-the-art enabling technologies, innovative applications, and potential opportunities that can benefit automakers, customers, and businesses. The current standardization efforts of the forefront enabling technologies, such as Wi-Fi 6 and 5G-cellular technologies are also reviewed. Different challenges in terms of cooperative computation, privacy/security, and over-the-air updates are discussed. Safety and non-safety applications are described and possible future opportunities that CV technology brings to our life are also highlighted

Towards Privacy Preserving IoT Environments: A Survey

The Internet of Things (IoT) is a network of Internet-enabled devices that can sense, communicate, and react to changes in their environment. Billions of these computing devices are connected to the Internet to exchange data between themselves and/or their infrastructure. IoT promises to enable a plethora of smart services in almost every aspect of our daily interactions and improve the overall quality of life. However, with the increasing wide adoption of IoT, come significant privacy concerns to lose control of how our data is collected and shared with others. As such, privacy is a core requirement in any IoT ecosystem and is a major concern that inhibits its widespread user adoption. The ultimate source of user discomfort is the lack of control over personal raw data that is directly streamed from sensors to the outside world. In this survey, we review existing research and proposed solutions to rising privacy concerns from a multipoint of view to identify the risks and mitigations. First, we provide an evaluation of privacy issues and concerns in IoT systems due to resource constraints. Second, we describe the proposed IoT solutions that embrace a variety of privacy concerns such as identification, tracking, monitoring, and profiling. Lastly, we discuss the mechanisms and architectures for protecting IoT data in case of mobility at the device layer, infrastructure/platform layer, and application layer

XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System

This work presents XBeats, a novel platform for real-time electrocardiogram monitoring and analysis that uses edge computing and machine learning for early anomaly detection. The platform encompasses a data acquisition ECG patch with 12 leads to collect heart signals, perform on-chip processing, and transmit the data to healthcare providers in real-time for further analysis. The ECG patch provides a dynamically configurable selection of the active ECG leads that could be transmitted to the backend monitoring system. The selection ranges from a single ECG lead to a complete 12-lead ECG testing configuration. XBeats implements a lightweight binary classifier for early anomaly detection to reduce the time to action should abnormal heart conditions occur. This initial detection phase is performed on the edge (i.e., the device paired with the patch) and alerts can be configured to notify designated healthcare providers. Further deep analysis can be performed on the full fidelity 12-lead data sent to the backend. A fully functional prototype of the XBeats has been implemented to demonstrate the feasibly and usability of the proposed system. Performance evaluation shows that XBeats can achieve up to 95.30% detection accuracy for abnormal conditions, while maintaining a high data acquisition rate of up to 441 samples per second. Moreover, the analytical results of the energy consumption profile show that the ECG patch provides up to 37 h of continuous 12-lead ECG streaming

Ubiquitous Health Monitoring Using Mobile Web Services

AbstractThe ever-increasing rise in the number of chronically ill people is a growing burden on healthcare institutions. People with chronic illnesses such as heart disease, being among the leading causes for morbidity and mortality, need constant monitoring of their health conditions. Remote health monitoring of patients residing in their homes helps reduce health–care costs. Current telemedicine solutions are used to remotely monitor vital signs such as blood pressure and blood sugar levels. These systems restrict the mobility of the patient, in addition to being limited in the number of vital signs that they support. The rapid developments in mobile devices coupled with the advancements in wireless access tech–nologies have made mobile devices an increasingly attractive platform for delivering remote patient health monitoring services. This paper demonstrates the capability of mobile devices to provide mobile, low-cost, and effcient remote health monitoring through a mobile Web services-based approach. The proposed approach shows an agile, flexible, interoperable, and economical alternative to existing remote health monitoring systems

A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior

The significant advancements in intelligent transportation systems (ITS) have contributed to the increased development in traffic modeling. These advancements include prediction and simulation models that are used to simulate and predict traffic behaviors on highway roads and urban networks. These models are capable of precise modeling of the current traffic status and accurate predictions of the future status based on varying traffic conditions. However, selecting the appropriate traffic model for a specific environmental setting is challenging and expensive due to the different requirements that need to be considered, such as accuracy, performance, and efficiency. In this research, we present a comprehensive literature review of the research related to traffic prediction and simulation models. We start by highlighting the challenges in the long-term and short-term prediction of traffic modeling. Then, we review the most common nonparametric prediction models. Lastly, we look into the existing literature on traffic simulation tools and traffic simulation algorithms. We summarize the available traffic models, define the required parameters, and discuss the limitations of each model. We hope that this survey serves as a useful resource for traffic management engineers, researchers, and practitioners in this domain